Financial Data Analysis Report

Generated on 2025-03-24 14:53:13

Dataset Overview

Basic Statistics
Rows 100
Columns 12
Numeric Columns 4
Categorical Columns 5
Datetime Columns 3
Memory Usage 0.04 MB
Missing Values

This dataset does not contain any missing values.

Column Types
Numeric Columns
  • Rating
  • Price_Numeric
  • Profit_Margin_Numeric
  • Discount_Numeric
Categorical Columns
  • Product_ID
  • Category
  • Discount
  • Stock
  • Supplier_Code
Datetime Columns
  • Price
  • Profit_Margin
  • Last_Restocked
Sample Data
Product_ID Category Price Discount Rating Stock Profit_Margin Supplier_Code Last_Restocked Price_Numeric Profit_Margin_Numeric Discount_Numeric
0 PROD-0001 Food $969.89 27.2% 2.177796 441 $82.82 SUP_D560 2023-01-01 969.89 82.82 27.2
1 PROD-0002 Home & Garden $777.38 7.2% 2.540391 607 $32.68 SUP_D851 2023-01-02 777.38 32.68 7.2
2 PROD-0003 Electronics $940.10 4.3% 4.404547 768 $89.66 SUP_E657 2023-01-03 940.10 89.66 4.3
3 PROD-0004 Food $895.88 14.7% 2.267688 324 $39.53 SUP_C352 2023-01-04 895.88 39.53 14.7
4 PROD-0005 Food $601.92 29.6% 1.677971 515 $2.07 SUP_B982 2023-01-05 601.92 2.07 29.6

Bivariate Analysis

This section examines relationships between variables, including correlations and feature-target relationships.

Correlation Analysis
Correlation Heatmap
Top Correlations
Variable 1 Variable 2 Correlation
Rating Discount_Numeric -0.2826
Profit_Margin_Numeric Discount_Numeric 0.0655
Rating Profit_Margin_Numeric -0.0507
Price_Numeric Profit_Margin_Numeric -0.0452
Price_Numeric Discount_Numeric -0.0155
Rating Price_Numeric 0.0025
Feature-Target Relationships

Contingency Table
Clothing Electronics Food Home & Garden
Clothing 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 1 0
Electronics 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
Food 1 0 0 1 1 0 0 0 1 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Home & Garden 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0

Chi-square p-value: 0.4404
There is no statistically significant association between these variables.

Product_ID vs Category

Contingency Table
Clothing Electronics Food Home & Garden
Clothing 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0
Electronics 0 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0
Food 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1
Home & Garden 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 0 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0

Chi-square p-value: 0.4404
There is no statistically significant association between these variables.

Price vs Category

Contingency Table
Clothing Electronics Food Home & Garden
Clothing 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 1 0 0
Electronics 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0
Food 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 1 0 2 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 1 2 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0
Home & Garden 0 1 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 2 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1

Chi-square p-value: 0.4973
There is no statistically significant association between these variables.

Discount vs Category

Group Statistics
Group Mean Std Count
Clothing 3.0416 1.0356 26
Electronics 3.1683 1.3195 20
Food 2.5186 1.1409 24
Home & Garden 3.0785 0.9592 30

ANOVA p-value: 0.1756
There is no statistically significant difference between groups.

Rating vs Category

Group Statistics
Group Mean Std Count
Clothing 543.7692 293.5081 26
Electronics 445.7000 252.1620 20
Food 550.4167 286.1422 24
Home & Garden 600.0667 278.2124 30

ANOVA p-value: 0.3024
There is no statistically significant difference between groups.

Stock vs Category

Contingency Table
Clothing Electronics Food Home & Garden
Clothing 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 0
Electronics 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
Food 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0
Home & Garden 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0

Chi-square p-value: 0.4404
There is no statistically significant association between these variables.

Profit_Margin vs Category

Contingency Table
Clothing Electronics Food Home & Garden
Clothing 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 1 0 0 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0
Electronics 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1
Food 0 0 0 0 0 0 1 0 0 1 0 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Home & Garden 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0

Chi-square p-value: 0.4404
There is no statistically significant association between these variables.

Supplier_Code vs Category

Contingency Table
Clothing Electronics Food Home & Garden
Clothing 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 1 0
Electronics 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
Food 1 0 0 1 1 0 0 0 1 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Home & Garden 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0

Chi-square p-value: 0.4404
There is no statistically significant association between these variables.

Last_Restocked vs Category

Group Statistics
Group Mean Std Count
Clothing 448.0915 316.0166 26
Electronics 490.4535 309.2182 20
Food 477.3438 316.3421 24
Home & Garden 541.1607 265.0188 30

ANOVA p-value: 0.7020
There is no statistically significant difference between groups.

Price_Numeric vs Category

Group Statistics
Group Mean Std Count
Clothing 50.3058 33.4867 26
Electronics 44.3060 33.9815 20
Food 58.8958 26.7640 24
Home & Garden 53.4847 30.6194 30

ANOVA p-value: 0.4740
There is no statistically significant difference between groups.

Profit_Margin_Numeric vs Category

Group Statistics
Group Mean Std Count
Clothing 16.0885 8.9954 26
Electronics 13.3350 8.6947 20
Food 16.8625 8.5010 24
Home & Garden 15.3433 9.5827 30

ANOVA p-value: 0.6111
There is no statistically significant difference between groups.

Discount_Numeric vs Category

Feature Importance Analysis

This section shows the importance of features in predicting target variables, calculated using machine learning techniques.

Feature Importance for Category
Method: Random Forest
Feature Importance for Category
Importance Values
Feature Importance Relative (%)
Rating 0.2703 100.0%
Price_Numeric 0.2524 93.4%
Profit_Margin_Numeric 0.2459 91.0%
Discount_Numeric 0.2314 85.6%

Multivariate Analysis

This section explores relationships between multiple variables using dimensionality reduction techniques like PCA and t-SNE.

Principal Component Analysis (PCA)
PCA Visualization
PCA Summary
Number of Components 2
Total Explained Variance 58.42%
PC1 Explained Variance 32.67%
PC2 Explained Variance 25.75%
Top Feature Contributions to PC1
Feature Contribution (%)
Feature Loadings Heatmap
PCA Loadings Heatmap
t-SNE Analysis
t-SNE Visualization
t-SNE Parameters
Number of Components 2
Perplexity 30.0
Learning Rate auto
Iterations 1000
About t-SNE

t-SNE is a nonlinear dimensionality reduction technique well-suited for visualizing high-dimensional data. Unlike PCA, t-SNE focuses on preserving local structure and revealing clusters.

Note: t-SNE should be used primarily for visualization, not for general dimensionality reduction or as input features for other algorithms.

Time Series Analysis

This section analyzes temporal patterns, trends, and seasonality in the data.

Time Series Statistics
Start Date 2023-01-01T00:00:00
End Date 2023-04-10T00:00:00
Duration 99 days
Mean 2.9525
Std. Dev. 1.1130
Min 1.0576
Max 4.9602
Trend increasing
Absolute Change 1.3256
Percent Change 60.87091524511602
Time Series Plot of Rating
Seasonality Analysis
Seasonality Decomposition
Decomposition Model additive
Period 7
Seasonal Strength 0.3937

Time Series Statistics
Start Date 2023-01-01T00:00:00
End Date 2023-04-10T00:00:00
Duration 99 days
Mean 491.5052
Std. Dev. 297.7975
Min 15.4700
Max 987.0200
Trend decreasing
Absolute Change -684.0300
Percent Change -70.52655455773335
Time Series Plot of Price_Numeric
Seasonality Analysis
Seasonality Decomposition
Decomposition Model additive
Period 7
Seasonal Strength 0.4228

Time Series Statistics
Start Date 2023-01-01T00:00:00
End Date 2023-04-10T00:00:00
Duration 99 days
Mean 52.1211
Std. Dev. 31.1566
Min 2.0700
Max 99.3000
Trend decreasing
Absolute Change -51.1800
Percent Change -61.79666747162521
Time Series Plot of Profit_Margin_Numeric
Seasonality Analysis
Seasonality Decomposition
Decomposition Model additive
Period 7
Seasonal Strength 0.4678

Time Series Statistics
Start Date 2023-01-01T00:00:00
End Date 2023-04-10T00:00:00
Duration 99 days
Mean 15.5000
Std. Dev. 8.9513
Min 0.2000
Max 29.6000
Trend decreasing
Absolute Change -1.6000
Percent Change -5.882352941176463
Time Series Plot of Discount_Numeric
Seasonality Analysis
Seasonality Decomposition
Decomposition Model additive
Period 7
Seasonal Strength 0.5597